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Posted to commits@commons.apache.org by tn...@apache.org on 2012/02/02 22:02:55 UTC
svn commit: r1239842 - in
/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression:
AbstractMultipleLinearRegression.java SimpleRegression.java
Author: tn
Date: Thu Feb 2 21:02:54 2012
New Revision: 1239842
URL: http://svn.apache.org/viewvc?rev=1239842&view=rev
Log:
Changed deprecated MathRuntimeException in package stat.regression
JIRA: MATH-459
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java
commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java?rev=1239842&r1=1239841&r2=1239842&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/AbstractMultipleLinearRegression.java Thu Feb 2 21:02:54 2012
@@ -16,8 +16,13 @@
*/
package org.apache.commons.math.stat.regression;
-import org.apache.commons.math.MathRuntimeException;
+import org.apache.commons.math.exception.DimensionMismatchException;
+import org.apache.commons.math.exception.MathIllegalArgumentException;
+import org.apache.commons.math.exception.NoDataException;
+import org.apache.commons.math.exception.NullArgumentException;
+import org.apache.commons.math.exception.NumberIsTooSmallException;
import org.apache.commons.math.exception.util.LocalizedFormats;
+import org.apache.commons.math.linear.NonSquareMatrixException;
import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.linear.Array2DRowRealMatrix;
import org.apache.commons.math.linear.RealVector;
@@ -87,20 +92,21 @@ public abstract class AbstractMultipleLi
* @param data input data array
* @param nobs number of observations (rows)
* @param nvars number of independent variables (columns, not counting y)
- * @throws IllegalArgumentException if the preconditions are not met
+ * @throws NullArgumentException if the data array is null
+ * @throws DimensionMismatchException if the length of the data array is not equal
+ * to <code>nobs * (nvars + 1)</code>
+ * @throws NumberIsTooSmallException if <code>nobs</code> is smaller than
+ * <code>nvars</code>
*/
public void newSampleData(double[] data, int nobs, int nvars) {
if (data == null) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NULL_NOT_ALLOWED);
+ throw new NullArgumentException();
}
if (data.length != nobs * (nvars + 1)) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.INVALID_REGRESSION_ARRAY, data.length, nobs, nvars);
+ throw new DimensionMismatchException(data.length, nobs * (nvars + 1));
}
if (nobs <= nvars) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS);
+ throw new NumberIsTooSmallException(nobs, nvars, false);
}
double[] y = new double[nobs];
final int cols = noIntercept ? nvars: nvars + 1;
@@ -123,16 +129,15 @@ public abstract class AbstractMultipleLi
* Loads new y sample data, overriding any previous data.
*
* @param y the array representing the y sample
- * @throws IllegalArgumentException if y is null or empty
+ * @throws NullArgumentException if y is null
+ * @throws NoDataException if y is empty
*/
protected void newYSampleData(double[] y) {
if (y == null) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NULL_NOT_ALLOWED);
+ throw new NullArgumentException();
}
if (y.length == 0) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NO_DATA);
+ throw new NoDataException();
}
this.Y = new ArrayRealVector(y);
}
@@ -158,16 +163,16 @@ public abstract class AbstractMultipleLi
* specifying a model including an intercept term.
* </p>
* @param x the rectangular array representing the x sample
- * @throws IllegalArgumentException if x is null, empty or not rectangular
+ * @throws NullArgumentException if x is null
+ * @throws NoDataException if x is empty
+ * @throws DimensionMismatchException if x is not rectangular
*/
protected void newXSampleData(double[][] x) {
if (x == null) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NULL_NOT_ALLOWED);
+ throw new NullArgumentException();
}
if (x.length == 0) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NO_DATA);
+ throw new NoDataException();
}
if (noIntercept) {
this.X = new Array2DRowRealMatrix(x, true);
@@ -176,9 +181,7 @@ public abstract class AbstractMultipleLi
final double[][] xAug = new double[x.length][nVars + 1];
for (int i = 0; i < x.length; i++) {
if (x[i].length != nVars) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.DIFFERENT_ROWS_LENGTHS,
- x[i].length, nVars);
+ throw new DimensionMismatchException(x[i].length, nVars);
}
xAug[i][0] = 1.0d;
System.arraycopy(x[i], 0, xAug[i], 1, nVars);
@@ -198,24 +201,27 @@ public abstract class AbstractMultipleLi
*
* @param x the [n,k] array representing the x data
* @param y the [n,1] array representing the y data
- * @throws IllegalArgumentException if any of the checks fail
- *
+ * @throws NullArgumentException if {@code x} or {@code y} is null
+ * @throws DimensionMismatchException if {@code x} and {@code y} do not
+ * have the same length
+ * @throws NoDataException if {@code x} or {@code y} are zero-length
+ * @throws MathIllegalArgumentException if the number of rows of {@code x}
+ * is not larger than the number of columns + 1
*/
protected void validateSampleData(double[][] x, double[] y) {
- if ((x == null) || (y == null) || (x.length != y.length)) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE,
- (x == null) ? 0 : x.length,
- (y == null) ? 0 : y.length);
+ if ((x == null) || (y == null)) {
+ throw new NullArgumentException();
+ }
+ if (x.length != y.length) {
+ throw new DimensionMismatchException(y.length, x.length);
}
if (x.length == 0) { // Must be no y data either
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NO_DATA);
+ throw new NoDataException();
}
if (x[0].length + 1 > x.length) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
- x.length, x[0].length);
+ throw new MathIllegalArgumentException(
+ LocalizedFormats.NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS,
+ x.length, x[0].length);
}
}
@@ -225,18 +231,16 @@ public abstract class AbstractMultipleLi
*
* @param x the [n,k] array representing the x sample
* @param covariance the [n,n] array representing the covariance matrix
- * @throws IllegalArgumentException if the number of rows in x is not equal
- * to the number of rows in covariance or covariance is not square.
+ * @throws DimensionMismatchException if the number of rows in x is not equal
+ * to the number of rows in covariance
+ * @throws NonSquareMatrixException if the covariance matrix is not square
*/
protected void validateCovarianceData(double[][] x, double[][] covariance) {
if (x.length != covariance.length) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, x.length, covariance.length);
+ throw new DimensionMismatchException(x.length, covariance.length);
}
if (covariance.length > 0 && covariance.length != covariance[0].length) {
- throw MathRuntimeException.createIllegalArgumentException(
- LocalizedFormats.NON_SQUARE_MATRIX,
- covariance.length, covariance[0].length);
+ throw new NonSquareMatrixException(covariance.length, covariance[0].length);
}
}
Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java?rev=1239842&r1=1239841&r2=1239842&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java (original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math/stat/regression/SimpleRegression.java Thu Feb 2 21:02:54 2012
@@ -18,7 +18,6 @@
package org.apache.commons.math.stat.regression;
import java.io.Serializable;
-import org.apache.commons.math.MathException;
import org.apache.commons.math.exception.OutOfRangeException;
import org.apache.commons.math.distribution.TDistribution;
import org.apache.commons.math.exception.MathIllegalArgumentException;
@@ -137,7 +136,7 @@ public class SimpleRegression implements
} else {
if( hasIntercept ){
final double fact1 = 1.0 + n;
- final double fact2 = (n) / (1.0 + n);
+ final double fact2 = n / (1.0 + n);
final double dx = x - xbar;
final double dy = y - ybar;
sumXX += dx * dx * fact2;
@@ -176,7 +175,7 @@ public class SimpleRegression implements
if (n > 0) {
if (hasIntercept) {
final double fact1 = n - 1.0;
- final double fact2 = (n) / (n - 1.0);
+ final double fact2 = n / (n - 1.0);
final double dx = x - xbar;
final double dy = y - ybar;
sumXX -= dx * dx * fact2;
@@ -609,9 +608,9 @@ public class SimpleRegression implements
* Bivariate Normal Distribution</a>.</p>
*
* @return half-width of 95% confidence interval for the slope estimate
- * @throws MathException if the confidence interval can not be computed.
+ * @throws OutOfRangeException if the confidence interval can not be computed.
*/
- public double getSlopeConfidenceInterval() throws MathException {
+ public double getSlopeConfidenceInterval() {
return getSlopeConfidenceInterval(0.05d);
}
@@ -639,15 +638,14 @@ public class SimpleRegression implements
* <code>Double.NaN</code>.
* </li>
* <li><code>(0 < alpha < 1)</code>; otherwise an
- * <code>IllegalArgumentException</code> is thrown.
+ * <code>OutOfRangeException</code> is thrown.
* </li></ul></p>
*
* @param alpha the desired significance level
* @return half-width of 95% confidence interval for the slope estimate
- * @throws MathException if the confidence interval can not be computed.
+ * @throws OutOfRangeException if the confidence interval can not be computed.
*/
- public double getSlopeConfidenceInterval(final double alpha)
- throws MathException {
+ public double getSlopeConfidenceInterval(final double alpha) {
if (alpha >= 1 || alpha <= 0) {
throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL,
alpha, 0, 1);
@@ -676,9 +674,10 @@ public class SimpleRegression implements
* <code>Double.NaN</code>.</p>
*
* @return significance level for slope/correlation
- * @throws MathException if the significance level can not be computed.
+ * @throws org.apache.commons.math.exception.MaxCountExceededException
+ * if the significance level can not be computed.
*/
- public double getSignificance() throws MathException {
+ public double getSignificance() {
TDistribution distribution = new TDistribution(n - 2);
return 2d * (1.0 - distribution.cumulativeProbability(
FastMath.abs(getSlope()) / getSlopeStdErr()));
@@ -724,16 +723,16 @@ public class SimpleRegression implements
if( FastMath.abs( sumXX ) > Precision.SAFE_MIN ){
final double[] params = new double[]{ getIntercept(), getSlope() };
final double mse = getMeanSquareError();
- final double _syy = sumYY + sumY * sumY / (n);
+ final double _syy = sumYY + sumY * sumY / n;
final double[] vcv = new double[]{
- mse * (xbar *xbar /sumXX + 1.0 / (n)),
+ mse * (xbar *xbar /sumXX + 1.0 / n),
-xbar*mse/sumXX,
mse/sumXX };
return new RegressionResults(
params, new double[][]{vcv}, true, n, 2,
sumY, _syy, getSumSquaredErrors(),true,false);
}else{
- final double[] params = new double[]{ sumY/(n), Double.NaN };
+ final double[] params = new double[]{ sumY / n, Double.NaN };
//final double mse = getMeanSquareError();
final double[] vcv = new double[]{
ybar / (n - 1.0),
@@ -797,7 +796,7 @@ public class SimpleRegression implements
if( variablesToInclude[0] != 1 && variablesToInclude[0] != 0 ){
throw new OutOfRangeException( variablesToInclude[0],0,1 );
}
- final double _mean = sumY * sumY / (n);
+ final double _mean = sumY * sumY / n;
final double _syy = sumYY + _mean;
if( variablesToInclude[0] == 0 ){
//just the mean
@@ -809,8 +808,8 @@ public class SimpleRegression implements
}else if( variablesToInclude[0] == 1){
//final double _syy = sumYY + sumY * sumY / ((double) n);
- final double _sxx = sumXX + sumX * sumX / (n);
- final double _sxy = sumXY + sumX * sumY / (n);
+ final double _sxx = sumXX + sumX * sumX / n;
+ final double _sxy = sumXY + sumX * sumY / n;
final double _sse = FastMath.max(0d, _syy - _sxy * _sxy / _sxx);
final double _mse = _sse/((n-1));
if( !Double.isNaN(_sxx) ){